Particle swarm optimization based on temporal-difference learning for solving multi-objective optimization problems

被引：0
作者：
Desong Zhang
Guangyu Zhu
机构：
[1] Fuzhou University,School of Mechanical Engineering and Automation
来源：
Computing | 2023年 / 105卷
关键词：
Multi-objective optimization; Particle swarm optimization; Reinforcement learning; Temporal-difference learning; 68W50; 68Q32; 90C29;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Multi-objective evolutionary algorithms have become the most important method to deal with multi-objective optimization problems (MOP). To improve the performance of particle swarm optimization (PSO) in addressing MOPs, a multi-objective PSO based on temporal-difference learning (TDLMOPSO) is proposed in this paper. The iteration process of TDLMOPSO is transformed into a Markov decision process, particles are treated as agents, each agent has a personal archive, the states are designed for the connection of actions, the actions of particles contain all necessary behavior of them: basic movement, jump out of local optimum, and local search, and the rewards depend on the relationship between particles’ positions and their personal archives. Besides, the external archive deletion strategy and the leader selection strategy are redesigned based on the unsupervised learning algorithm to enhance the diversity of solutions in the external archive. The effectiveness of TDLMOPSO is verified by applying it with other seven advanced multi-objective algorithms in MOP benchmark test suites. Furthermore, the time complexity and parameter sensitivity of TDLMOPSO are analyzed.
引用
收藏
页码：1795 / 1820
页数：25
相关论文
共 86 条
  • [11] Han H(2020)A decomposition-based many-objective artificial bee colony algorithm with reinforcement learning Appl Soft Comput 86 724-738
  • [12] Ding R(2022)A self-learning bee colony and genetic algorithm hybrid for cloud manufacturing services Computing 104 623-634
  • [13] Dong H(2021)Combined use of coral reefs optimization and reinforcement learning for improving resource utilization and load balancing in cloud environments Computing 103 2698-2711
  • [14] He J(2020)Adaptive offspring generation for evolutionary large-scale multiobjective optimization IEEE Trans Syst Man Cyber Syst 52 173-195
  • [15] Li T(2018)A competitive mechanism based multi-objective particle swarm optimizer with fast convergence Inf Sci 427 67-81
  • [16] Zhu Q(2021)Large-scale evolutionary multiobjective optimization assisted by directed sampling IEEE Trans Evol Comput 25 257-271
  • [17] Lin Q(2022)Local model-based pareto front estimation for multiobjective optimization IEEE Trans Syst Man Cyber Syst 53 undefined-undefined
  • [18] Chen W(2020)An adaptive reference vector-guided evolutionary algorithm using growing neural gas for many-objective optimization of irregular problems IEEE Trans Cyber 52 undefined-undefined
  • [19] Wong K-C(2000)Comparison of multiobjective evolutionary algorithms: Empirical results Evol Comput 8 undefined-undefined
  • [20] Coello CAC(2017)A benchmark test suite for evolutionary many-objective optimization Complex Intell Syst 3 undefined-undefined
123456789